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- W4288804561 abstract "We present Neural Strands, a novel learning framework for modeling accurate hair geometry and appearance from multi-view image inputs. The learned hair model can be rendered in real-time from any viewpoint with high-fidelity view-dependent effects. Our model achieves intuitive shape and style control unlike volumetric counterparts. To enable these properties, we propose a novel hair representation based on a neural scalp texture that encodes the geometry and appearance of individual strands at each texel location. Furthermore, we introduce a novel neural rendering framework based on rasterization of the learned hair strands. Our neural rendering is strand-accurate and anti-aliased, making the rendering view-consistent and photorealistic. Combining appearance with a multi-view geometric prior, we enable, for the first time, the joint learning of appearance and explicit hair geometry from a multi-view setup. We demonstrate the efficacy of our approach in terms of fidelity and efficiency for various hairstyles." @default.
- W4288804561 created "2022-07-30" @default.
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- W4288804561 date "2022-07-28" @default.
- W4288804561 modified "2023-09-26" @default.
- W4288804561 title "Neural Strands: Learning Hair Geometry and Appearance from Multi-View Images" @default.
- W4288804561 doi "https://doi.org/10.48550/arxiv.2207.14067" @default.
- W4288804561 hasPublicationYear "2022" @default.
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